Image Style Migration and Art Creation Based on VGG19 Deep Learning Algorithm

The origin of image style migration techniques can be traced back to the intersection of computer graphics and art creation. Early research explored stylized representations through mathematical modeling and image processing algorithms, but their effectiveness was limited by the limitations of manually designed features. The introduction of deep learning has revolutionized the implementation path of this technique, especially the convolutional neural network (CNN), which provides a new paradigm for decoupling and reorganizing style and content by virtue of its multilevel feature extraction capability. In this paper, we construct an image style migration model based on VGG19 network, separate the content and style representations by pre-training the deep feature space of the network, and use the joint optimization of the content loss function and the style loss function to realize the efficient fusion of the content of the source image and the target style. Experiments show that the method can accurately capture art style texture features while preserving the structural integrity of the content, and the generation quality is significantly better than the traditional algorithm. More stylized content will be developed subsequently, with important applications in image style examples and references, and graphic creative design, laying the foundation for the innovative and stylized development of graphic image design creativity. Using image style migration means can create more series of style art works, providing more creative inspiration and creative methods for art practitioners. It can be widely used in commercial illustration design, digital photography, digital painting, packaging design and other aspects.